"""Test ChatOpenAI chat model."""

import base64
import json
import os
from collections.abc import AsyncIterator
from pathlib import Path
from textwrap import dedent
from typing import Any, Literal, cast

import httpx
import openai
import pytest
from langchain_core.callbacks import CallbackManager
from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    BaseMessageChunk,
    HumanMessage,
    SystemMessage,
    ToolCall,
    ToolMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
from langchain_tests.integration_tests.chat_models import (
    _validate_tool_call_message,
    magic_function,
)
from pydantic import BaseModel, Field, field_validator
from typing_extensions import TypedDict

from langchain_openai import ChatOpenAI
from tests.unit_tests.fake.callbacks import FakeCallbackHandler

MAX_TOKEN_COUNT = 100


@pytest.mark.scheduled
def test_chat_openai() -> None:
    """Test ChatOpenAI wrapper."""
    chat = ChatOpenAI(
        temperature=0.7,
        base_url=None,
        organization=None,
        openai_proxy=None,
        timeout=10.0,
        max_retries=3,
        http_client=None,
        n=1,
        max_tokens=MAX_TOKEN_COUNT,  # type: ignore[call-arg]
        default_headers=None,
        default_query=None,
    )
    message = HumanMessage(content="Hello")
    response = chat.invoke([message])
    assert isinstance(response, BaseMessage)
    assert isinstance(response.content, str)


def test_chat_openai_model() -> None:
    """Test ChatOpenAI wrapper handles model_name."""
    chat = ChatOpenAI(model="foo")
    assert chat.model_name == "foo"
    chat = ChatOpenAI(model_name="bar")  # type: ignore[call-arg]
    assert chat.model_name == "bar"


def test_callable_api_key(monkeypatch: pytest.MonkeyPatch) -> None:
    original_key = os.environ["OPENAI_API_KEY"]

    calls = {"sync": 0}

    def get_openai_api_key() -> str:
        calls["sync"] += 1
        return original_key

    monkeypatch.delenv("OPENAI_API_KEY")

    model = ChatOpenAI(model="gpt-4.1-mini", api_key=get_openai_api_key)
    response = model.invoke("hello")
    assert isinstance(response, AIMessage)
    assert calls["sync"] == 1


async def test_callable_api_key_async(monkeypatch: pytest.MonkeyPatch) -> None:
    original_key = os.environ["OPENAI_API_KEY"]

    calls = {"sync": 0, "async": 0}

    def get_openai_api_key() -> str:
        calls["sync"] += 1
        return original_key

    async def get_openai_api_key_async() -> str:
        calls["async"] += 1
        return original_key

    monkeypatch.delenv("OPENAI_API_KEY")

    model = ChatOpenAI(model="gpt-4.1-mini", api_key=get_openai_api_key)
    response = model.invoke("hello")
    assert isinstance(response, AIMessage)
    assert calls["sync"] == 1

    response = await model.ainvoke("hello")
    assert isinstance(response, AIMessage)
    assert calls["sync"] == 2

    model = ChatOpenAI(model="gpt-4.1-mini", api_key=get_openai_api_key_async)
    async_response = await model.ainvoke("hello")
    assert isinstance(async_response, AIMessage)
    assert calls["async"] == 1

    with pytest.raises(ValueError):
        # We do not create a sync callable from an async one
        _ = model.invoke("hello")


@pytest.mark.parametrize("use_responses_api", [False, True])
def test_chat_openai_system_message(use_responses_api: bool) -> None:
    """Test ChatOpenAI wrapper with system message."""
    chat = ChatOpenAI(use_responses_api=use_responses_api, max_tokens=MAX_TOKEN_COUNT)  # type: ignore[call-arg]
    system_message = SystemMessage(content="You are to chat with the user.")
    human_message = HumanMessage(content="Hello")
    response = chat.invoke([system_message, human_message])
    assert isinstance(response, BaseMessage)
    assert isinstance(response.text, str)


@pytest.mark.scheduled
def test_chat_openai_generate() -> None:
    """Test ChatOpenAI wrapper with generate."""
    chat = ChatOpenAI(max_tokens=MAX_TOKEN_COUNT, n=2)  # type: ignore[call-arg]
    message = HumanMessage(content="Hello")
    response = chat.generate([[message], [message]])
    assert isinstance(response, LLMResult)
    assert len(response.generations) == 2
    assert response.llm_output
    for generations in response.generations:
        assert len(generations) == 2
        for generation in generations:
            assert isinstance(generation, ChatGeneration)
            assert isinstance(generation.text, str)
            assert generation.text == generation.message.content


@pytest.mark.scheduled
def test_chat_openai_multiple_completions() -> None:
    """Test ChatOpenAI wrapper with multiple completions."""
    chat = ChatOpenAI(max_tokens=MAX_TOKEN_COUNT, n=5)  # type: ignore[call-arg]
    message = HumanMessage(content="Hello")
    response = chat._generate([message])
    assert isinstance(response, ChatResult)
    assert len(response.generations) == 5
    for generation in response.generations:
        assert isinstance(generation.message, BaseMessage)
        assert isinstance(generation.message.content, str)


@pytest.mark.scheduled
@pytest.mark.parametrize("use_responses_api", [False, True])
def test_chat_openai_streaming(use_responses_api: bool) -> None:
    """Test that streaming correctly invokes on_llm_new_token callback."""
    callback_handler = FakeCallbackHandler()
    callback_manager = CallbackManager([callback_handler])
    chat = ChatOpenAI(
        max_tokens=MAX_TOKEN_COUNT,  # type: ignore[call-arg]
        streaming=True,
        temperature=0,
        callbacks=callback_manager,
        verbose=True,
        use_responses_api=use_responses_api,
    )
    message = HumanMessage(content="Hello")
    response = chat.invoke([message])
    assert callback_handler.llm_streams > 0
    assert isinstance(response, BaseMessage)


@pytest.mark.scheduled
def test_chat_openai_streaming_generation_info() -> None:
    """Test that generation info is preserved when streaming."""

    class _FakeCallback(FakeCallbackHandler):
        saved_things: dict = {}

        def on_llm_end(self, *args: Any, **kwargs: Any) -> Any:
            # Save the generation
            self.saved_things["generation"] = args[0]

    callback = _FakeCallback()
    callback_manager = CallbackManager([callback])
    chat = ChatOpenAI(max_tokens=2, temperature=0, callbacks=callback_manager)  # type: ignore[call-arg]
    list(chat.stream("hi"))
    generation = callback.saved_things["generation"]
    # `Hello!` is two tokens, assert that is what is returned
    assert generation.generations[0][0].text == "Hello!"


def test_chat_openai_llm_output_contains_model_name() -> None:
    """Test llm_output contains model_name."""
    chat = ChatOpenAI(max_tokens=MAX_TOKEN_COUNT)  # type: ignore[call-arg]
    message = HumanMessage(content="Hello")
    llm_result = chat.generate([[message]])
    assert llm_result.llm_output is not None
    assert llm_result.llm_output["model_name"] == chat.model_name


def test_chat_openai_streaming_llm_output_contains_model_name() -> None:
    """Test llm_output contains model_name."""
    chat = ChatOpenAI(max_tokens=MAX_TOKEN_COUNT, streaming=True)  # type: ignore[call-arg]
    message = HumanMessage(content="Hello")
    llm_result = chat.generate([[message]])
    assert llm_result.llm_output is not None
    assert llm_result.llm_output["model_name"] == chat.model_name


def test_chat_openai_invalid_streaming_params() -> None:
    """Test that streaming correctly invokes on_llm_new_token callback."""
    with pytest.raises(ValueError):
        ChatOpenAI(max_tokens=MAX_TOKEN_COUNT, streaming=True, temperature=0, n=5)  # type: ignore[call-arg]


@pytest.mark.scheduled
@pytest.mark.parametrize("use_responses_api", [False, True])
async def test_openai_abatch_tags(use_responses_api: bool) -> None:
    """Test batch tokens from ChatOpenAI."""
    llm = ChatOpenAI(max_tokens=MAX_TOKEN_COUNT, use_responses_api=use_responses_api)  # type: ignore[call-arg]

    result = await llm.abatch(
        ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
    )
    for token in result:
        assert isinstance(token.text, str)


@pytest.mark.flaky(retries=3, delay=1)
def test_openai_invoke() -> None:
    """Test invoke tokens from ChatOpenAI."""
    llm = ChatOpenAI(
        model="gpt-5-nano",
        service_tier="flex",  # Also test service_tier
        max_retries=3,  # Add retries for 503 capacity errors
    )

    result = llm.invoke("Hello", config={"tags": ["foo"]})
    assert isinstance(result.content, str)

    usage_metadata = result.usage_metadata  # type: ignore[attr-defined]

    # assert no response headers if include_response_headers is not set
    assert "headers" not in result.response_metadata
    assert usage_metadata is not None
    flex_input = usage_metadata.get("input_token_details", {}).get("flex")
    assert isinstance(flex_input, int)
    assert flex_input > 0
    assert flex_input == usage_metadata.get("input_tokens")
    flex_output = usage_metadata.get("output_token_details", {}).get("flex")
    assert isinstance(flex_output, int)
    assert flex_output > 0
    # GPT-5-nano/reasoning model specific. Remove if model used in test changes.
    flex_reasoning = usage_metadata.get("output_token_details", {}).get(
        "flex_reasoning"
    )
    assert isinstance(flex_reasoning, int)
    assert flex_reasoning > 0
    assert flex_reasoning + flex_output == usage_metadata.get("output_tokens")


@pytest.mark.flaky(retries=3, delay=1)
def test_stream() -> None:
    """Test streaming tokens from OpenAI."""
    llm = ChatOpenAI(
        model="gpt-5-nano",
        service_tier="flex",  # Also test service_tier
        max_retries=3,  # Add retries for 503 capacity errors
    )

    full: BaseMessageChunk | None = None
    for chunk in llm.stream("I'm Pickle Rick"):
        assert isinstance(chunk.content, str)
        full = chunk if full is None else full + chunk
    assert isinstance(full, AIMessageChunk)
    assert full.response_metadata.get("finish_reason") is not None
    assert full.response_metadata.get("model_name") is not None

    # check token usage
    aggregate: BaseMessageChunk | None = None
    chunks_with_token_counts = 0
    chunks_with_response_metadata = 0
    for chunk in llm.stream("Hello"):
        assert isinstance(chunk.content, str)
        aggregate = chunk if aggregate is None else aggregate + chunk
        assert isinstance(chunk, AIMessageChunk)
        if chunk.usage_metadata is not None:
            chunks_with_token_counts += 1
        if chunk.response_metadata and not set(chunk.response_metadata.keys()).issubset(
            {"model_provider", "output_version"}
        ):
            chunks_with_response_metadata += 1
    if chunks_with_token_counts != 1 or chunks_with_response_metadata != 1:
        msg = (
            "Expected exactly one chunk with metadata. "
            "AIMessageChunk aggregation can add these metadata. Check that "
            "this is behaving properly."
        )
        raise AssertionError(msg)
    assert isinstance(aggregate, AIMessageChunk)
    assert aggregate.usage_metadata is not None
    assert aggregate.usage_metadata["input_tokens"] > 0
    assert aggregate.usage_metadata["output_tokens"] > 0
    assert aggregate.usage_metadata["total_tokens"] > 0
    assert aggregate.usage_metadata.get("input_token_details", {}).get("flex", 0) > 0  # type: ignore[operator]
    assert aggregate.usage_metadata.get("output_token_details", {}).get("flex", 0) > 0  # type: ignore[operator]
    assert (
        aggregate.usage_metadata.get("output_token_details", {}).get(  # type: ignore[operator]
            "flex_reasoning", 0
        )
        > 0
    )
    assert aggregate.usage_metadata.get("output_token_details", {}).get(  # type: ignore[operator]
        "flex_reasoning", 0
    ) + aggregate.usage_metadata.get("output_token_details", {}).get(
        "flex", 0
    ) == aggregate.usage_metadata.get("output_tokens")


async def test_astream() -> None:
    """Test streaming tokens from OpenAI."""

    async def _test_stream(stream: AsyncIterator, expect_usage: bool) -> None:
        full: BaseMessageChunk | None = None
        chunks_with_token_counts = 0
        chunks_with_response_metadata = 0
        async for chunk in stream:
            assert isinstance(chunk.content, str)
            full = chunk if full is None else full + chunk
            assert isinstance(chunk, AIMessageChunk)
            if chunk.usage_metadata is not None:
                chunks_with_token_counts += 1
            if chunk.response_metadata and not set(
                chunk.response_metadata.keys()
            ).issubset({"model_provider", "output_version"}):
                chunks_with_response_metadata += 1
        assert isinstance(full, AIMessageChunk)
        if chunks_with_response_metadata != 1:
            msg = (
                "Expected exactly one chunk with metadata. "
                "AIMessageChunk aggregation can add these metadata. Check that "
                "this is behaving properly."
            )
            raise AssertionError(msg)
        assert full.response_metadata.get("finish_reason") is not None
        assert full.response_metadata.get("model_name") is not None
        if expect_usage:
            if chunks_with_token_counts != 1:
                msg = (
                    "Expected exactly one chunk with token counts. "
                    "AIMessageChunk aggregation adds counts. Check that "
                    "this is behaving properly."
                )
                raise AssertionError(msg)
            assert full.usage_metadata is not None
            assert full.usage_metadata["input_tokens"] > 0
            assert full.usage_metadata["output_tokens"] > 0
            assert full.usage_metadata["total_tokens"] > 0
        else:
            assert chunks_with_token_counts == 0
            assert full.usage_metadata is None

    llm = ChatOpenAI(model="gpt-4.1-mini", temperature=0, max_tokens=MAX_TOKEN_COUNT)  # type: ignore[call-arg]
    await _test_stream(llm.astream("Hello", stream_usage=False), expect_usage=False)
    await _test_stream(
        llm.astream("Hello", stream_options={"include_usage": True}), expect_usage=True
    )
    await _test_stream(llm.astream("Hello", stream_usage=True), expect_usage=True)
    llm = ChatOpenAI(
        model="gpt-4.1-mini",
        temperature=0,
        max_tokens=MAX_TOKEN_COUNT,  # type: ignore[call-arg]
        model_kwargs={"stream_options": {"include_usage": True}},
    )
    await _test_stream(llm.astream("Hello"), expect_usage=True)
    await _test_stream(
        llm.astream("Hello", stream_options={"include_usage": False}),
        expect_usage=False,
    )
    llm = ChatOpenAI(
        model="gpt-4.1-mini",
        temperature=0,
        max_tokens=MAX_TOKEN_COUNT,  # type: ignore[call-arg]
        stream_usage=True,
    )
    await _test_stream(llm.astream("Hello"), expect_usage=True)
    await _test_stream(llm.astream("Hello", stream_usage=False), expect_usage=False)


@pytest.mark.parametrize("streaming", [False, True])
def test_flex_usage_responses(streaming: bool) -> None:
    llm = ChatOpenAI(
        model="gpt-5-nano",
        service_tier="flex",
        max_retries=3,
        use_responses_api=True,
        streaming=streaming,
    )
    result = llm.invoke("Hello")
    assert result.usage_metadata
    flex_input = result.usage_metadata.get("input_token_details", {}).get("flex")
    flex_output = result.usage_metadata.get("output_token_details", {}).get("flex")
    flex_reasoning = result.usage_metadata.get("output_token_details", {}).get(
        "flex_reasoning"
    )
    assert isinstance(flex_input, int)
    assert isinstance(flex_output, int)
    assert isinstance(flex_reasoning, int)
    assert flex_output + flex_reasoning == result.usage_metadata.get("output_tokens")


async def test_abatch_tags() -> None:
    """Test batch tokens from ChatOpenAI."""
    llm = ChatOpenAI()

    result = await llm.abatch(
        ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
    )
    for token in result:
        assert isinstance(token.content, str)


def test_response_metadata() -> None:
    llm = ChatOpenAI()
    result = llm.invoke([HumanMessage(content="I'm PickleRick")], logprobs=True)
    assert result.response_metadata
    assert all(
        k in result.response_metadata
        for k in (
            "token_usage",
            "model_name",
            "logprobs",
            "system_fingerprint",
            "finish_reason",
            "service_tier",
        )
    )
    assert "content" in result.response_metadata["logprobs"]


async def test_async_response_metadata() -> None:
    llm = ChatOpenAI()
    result = await llm.ainvoke([HumanMessage(content="I'm PickleRick")], logprobs=True)
    assert result.response_metadata
    assert all(
        k in result.response_metadata
        for k in (
            "token_usage",
            "model_name",
            "logprobs",
            "system_fingerprint",
            "finish_reason",
            "service_tier",
        )
    )
    assert "content" in result.response_metadata["logprobs"]


def test_response_metadata_streaming() -> None:
    llm = ChatOpenAI()
    full: BaseMessageChunk | None = None
    for chunk in llm.stream("I'm Pickle Rick", logprobs=True):
        assert isinstance(chunk.content, str)
        full = chunk if full is None else full + chunk
    assert all(
        k in cast(BaseMessageChunk, full).response_metadata
        for k in ("logprobs", "finish_reason", "service_tier")
    )
    assert "content" in cast(BaseMessageChunk, full).response_metadata["logprobs"]


async def test_async_response_metadata_streaming() -> None:
    llm = ChatOpenAI()
    full: BaseMessageChunk | None = None
    async for chunk in llm.astream("I'm Pickle Rick", logprobs=True):
        assert isinstance(chunk.content, str)
        full = chunk if full is None else full + chunk
    assert all(
        k in cast(BaseMessageChunk, full).response_metadata
        for k in ("logprobs", "finish_reason", "service_tier")
    )
    assert "content" in cast(BaseMessageChunk, full).response_metadata["logprobs"]


class GenerateUsername(BaseModel):
    "Get a username based on someone's name and hair color."

    name: str
    hair_color: str


class MakeASandwich(BaseModel):
    "Make a sandwich given a list of ingredients."

    bread_type: str
    cheese_type: str
    condiments: list[str]
    vegetables: list[str]


def test_tool_use() -> None:
    llm = ChatOpenAI(model="gpt-5-nano", temperature=0)
    llm_with_tool = llm.bind_tools(tools=[GenerateUsername], tool_choice=True)
    msgs: list = [HumanMessage("Sally has green hair, what would her username be?")]
    ai_msg = llm_with_tool.invoke(msgs)

    assert isinstance(ai_msg, AIMessage)
    assert isinstance(ai_msg.tool_calls, list)
    assert len(ai_msg.tool_calls) == 1
    tool_call = ai_msg.tool_calls[0]
    assert "args" in tool_call

    tool_msg = ToolMessage("sally_green_hair", tool_call_id=ai_msg.tool_calls[0]["id"])
    msgs.extend([ai_msg, tool_msg])
    llm_with_tool.invoke(msgs)

    # Test streaming
    ai_messages = llm_with_tool.stream(msgs)
    first = True
    for message in ai_messages:
        if first:
            gathered = message
            first = False
        else:
            gathered = gathered + message  # type: ignore
    assert isinstance(gathered, AIMessageChunk)
    assert isinstance(gathered.tool_call_chunks, list)
    assert len(gathered.tool_call_chunks) == 1
    tool_call_chunk = gathered.tool_call_chunks[0]
    assert "args" in tool_call_chunk
    assert gathered.content_blocks == gathered.tool_calls

    streaming_tool_msg = ToolMessage(
        "sally_green_hair", tool_call_id=gathered.tool_calls[0]["id"]
    )
    msgs.extend([gathered, streaming_tool_msg])
    llm_with_tool.invoke(msgs)


@pytest.mark.parametrize("use_responses_api", [False, True])
def test_manual_tool_call_msg(use_responses_api: bool) -> None:
    """Test passing in manually construct tool call message."""
    llm = ChatOpenAI(
        model="gpt-5-nano", temperature=0, use_responses_api=use_responses_api
    )
    llm_with_tool = llm.bind_tools(tools=[GenerateUsername])
    msgs: list = [
        HumanMessage("Sally has green hair, what would her username be?"),
        AIMessage(
            content="",
            tool_calls=[
                ToolCall(
                    name="GenerateUsername",
                    args={"name": "Sally", "hair_color": "green"},
                    id="foo",
                    type="tool_call",
                )
            ],
        ),
        ToolMessage("sally_green_hair", tool_call_id="foo"),
    ]
    output: AIMessage = cast(AIMessage, llm_with_tool.invoke(msgs))
    assert output.content
    # Should not have called the tool again.
    assert not output.tool_calls
    assert not output.invalid_tool_calls

    # OpenAI should error when tool call id doesn't match across AIMessage and
    # ToolMessage
    msgs = [
        HumanMessage("Sally has green hair, what would her username be?"),
        AIMessage(
            content="",
            tool_calls=[
                ToolCall(
                    name="GenerateUsername",
                    args={"name": "Sally", "hair_color": "green"},
                    id="bar",
                    type="tool_call",
                )
            ],
        ),
        ToolMessage("sally_green_hair", tool_call_id="foo"),
    ]
    with pytest.raises(Exception):
        llm_with_tool.invoke(msgs)


@pytest.mark.parametrize("use_responses_api", [False, True])
def test_bind_tools_tool_choice(use_responses_api: bool) -> None:
    """Test passing in manually construct tool call message."""
    llm = ChatOpenAI(
        model="gpt-5-nano", temperature=0, use_responses_api=use_responses_api
    )
    for tool_choice in ("any", "required"):
        llm_with_tools = llm.bind_tools(
            tools=[GenerateUsername, MakeASandwich], tool_choice=tool_choice
        )
        msg = cast(AIMessage, llm_with_tools.invoke("how are you"))
        assert msg.tool_calls

    llm_with_tools = llm.bind_tools(tools=[GenerateUsername, MakeASandwich])
    msg = cast(AIMessage, llm_with_tools.invoke("how are you"))
    assert not msg.tool_calls


def test_disable_parallel_tool_calling() -> None:
    llm = ChatOpenAI(model="gpt-5-nano")
    llm_with_tools = llm.bind_tools([GenerateUsername], parallel_tool_calls=False)
    result = llm_with_tools.invoke(
        "Use the GenerateUsername tool to generate user names for:\n\n"
        "Sally with green hair\n"
        "Bob with blue hair"
    )
    assert isinstance(result, AIMessage)
    assert len(result.tool_calls) == 1


@pytest.mark.parametrize("model", ["gpt-4o-mini", "o1", "gpt-4", "gpt-5-nano"])
def test_openai_structured_output(model: str) -> None:
    class MyModel(BaseModel):
        """A Person"""

        name: str
        age: int

    llm = ChatOpenAI(model=model).with_structured_output(MyModel)
    result = llm.invoke("I'm a 27 year old named Erick")
    assert isinstance(result, MyModel)
    assert result.name == "Erick"
    assert result.age == 27


def test_openai_proxy() -> None:
    """Test ChatOpenAI with proxy."""
    chat_openai = ChatOpenAI(openai_proxy="http://localhost:8080")
    mounts = chat_openai.client._client._client._mounts
    assert len(mounts) == 1
    for value in mounts.values():
        proxy = value._pool._proxy_url.origin
        assert proxy.scheme == b"http"
        assert proxy.host == b"localhost"
        assert proxy.port == 8080

    async_client_mounts = chat_openai.async_client._client._client._mounts
    assert len(async_client_mounts) == 1
    for value in async_client_mounts.values():
        proxy = value._pool._proxy_url.origin
        assert proxy.scheme == b"http"
        assert proxy.host == b"localhost"
        assert proxy.port == 8080


@pytest.mark.parametrize("use_responses_api", [False, True])
def test_openai_response_headers(use_responses_api: bool) -> None:
    """Test ChatOpenAI response headers."""
    chat_openai = ChatOpenAI(
        include_response_headers=True, use_responses_api=use_responses_api
    )
    query = "I'm Pickle Rick"
    result = chat_openai.invoke(query, max_tokens=MAX_TOKEN_COUNT)  # type: ignore[call-arg]
    headers = result.response_metadata["headers"]
    assert headers
    assert isinstance(headers, dict)
    assert "content-type" in headers

    # Stream
    full: BaseMessageChunk | None = None
    for chunk in chat_openai.stream(query, max_tokens=MAX_TOKEN_COUNT):  # type: ignore[call-arg]
        full = chunk if full is None else full + chunk
    assert isinstance(full, AIMessage)
    headers = full.response_metadata["headers"]
    assert headers
    assert isinstance(headers, dict)
    assert "content-type" in headers


@pytest.mark.parametrize("use_responses_api", [False, True])
async def test_openai_response_headers_async(use_responses_api: bool) -> None:
    """Test ChatOpenAI response headers."""
    chat_openai = ChatOpenAI(
        include_response_headers=True, use_responses_api=use_responses_api
    )
    query = "I'm Pickle Rick"
    result = await chat_openai.ainvoke(query, max_tokens=MAX_TOKEN_COUNT)  # type: ignore[call-arg]
    headers = result.response_metadata["headers"]
    assert headers
    assert isinstance(headers, dict)
    assert "content-type" in headers

    # Stream
    full: BaseMessageChunk | None = None
    async for chunk in chat_openai.astream(query, max_tokens=MAX_TOKEN_COUNT):  # type: ignore[call-arg]
        full = chunk if full is None else full + chunk
    assert isinstance(full, AIMessage)
    headers = full.response_metadata["headers"]
    assert headers
    assert isinstance(headers, dict)
    assert "content-type" in headers


def test_image_token_counting_jpeg() -> None:
    model = ChatOpenAI(model="gpt-4o", temperature=0)
    image_url = "https://raw.githubusercontent.com/langchain-ai/docs/9f99bb977307a1bd5efeb8dc6b67eb13904c4af1/src/oss/images/checkpoints.jpg"
    message = HumanMessage(
        content=[
            {"type": "text", "text": "describe the weather in this image"},
            {"type": "image_url", "image_url": {"url": image_url}},
        ]
    )
    expected = cast(AIMessage, model.invoke([message])).usage_metadata[  # type: ignore[index]
        "input_tokens"
    ]
    actual = model.get_num_tokens_from_messages([message])
    assert expected == actual

    image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
    message = HumanMessage(
        content=[
            {"type": "text", "text": "describe the weather in this image"},
            {
                "type": "image_url",
                "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
            },
        ]
    )
    expected = cast(AIMessage, model.invoke([message])).usage_metadata[  # type: ignore[index]
        "input_tokens"
    ]
    actual = model.get_num_tokens_from_messages([message])
    assert expected == actual


def test_image_token_counting_png() -> None:
    model = ChatOpenAI(model="gpt-4o", temperature=0)
    image_url = "https://raw.githubusercontent.com/langchain-ai/docs/4d11d08b6b0e210bd456943f7a22febbd168b543/src/images/agentic-rag-output.png"
    message = HumanMessage(
        content=[
            {"type": "text", "text": "how many dice are in this image"},
            {"type": "image_url", "image_url": {"url": image_url}},
        ]
    )
    expected = cast(AIMessage, model.invoke([message])).usage_metadata[  # type: ignore[index]
        "input_tokens"
    ]
    actual = model.get_num_tokens_from_messages([message])
    assert expected == actual

    image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
    message = HumanMessage(
        content=[
            {"type": "text", "text": "how many dice are in this image"},
            {
                "type": "image_url",
                "image_url": {"url": f"data:image/png;base64,{image_data}"},
            },
        ]
    )
    expected = cast(AIMessage, model.invoke([message])).usage_metadata[  # type: ignore[index]
        "input_tokens"
    ]
    actual = model.get_num_tokens_from_messages([message])
    assert expected == actual


@pytest.mark.parametrize("use_responses_api", [False, True])
def test_tool_calling_strict(use_responses_api: bool) -> None:
    """Test tool calling with strict=True.

    Responses API appears to have fewer constraints on schema when strict=True.
    """

    class magic_function_notrequired_arg(BaseModel):  # noqa: N801
        """Applies a magic function to an input."""

        input: int | None = Field(default=None)

    model = ChatOpenAI(
        model="gpt-5-nano", temperature=0, use_responses_api=use_responses_api
    )
    # N.B. magic_function adds metadata to schema (min/max for number fields)
    model_with_tools = model.bind_tools([magic_function], strict=True)
    # Having a not-required argument in the schema remains invalid.
    model_with_invalid_tool_schema = model.bind_tools(
        [magic_function_notrequired_arg], strict=True
    )

    # Test invoke
    query = "What is the value of magic_function(3)? Use the tool."
    response = model_with_tools.invoke(query)
    _validate_tool_call_message(response)

    # Test invalid tool schema
    with pytest.raises(openai.BadRequestError):
        model_with_invalid_tool_schema.invoke(query)

    # Test stream
    full: BaseMessageChunk | None = None
    for chunk in model_with_tools.stream(query):
        full = chunk if full is None else full + chunk  # type: ignore
    assert isinstance(full, AIMessage)
    _validate_tool_call_message(full)

    # Test invalid tool schema
    with pytest.raises(openai.BadRequestError):
        next(model_with_invalid_tool_schema.stream(query))


@pytest.mark.parametrize("use_responses_api", [False, True])
@pytest.mark.parametrize(
    ("model", "method"),
    [("gpt-4o", "function_calling"), ("gpt-4o-2024-08-06", "json_schema")],
)
def test_structured_output_strict(
    model: str,
    method: Literal["function_calling", "json_schema"],
    use_responses_api: bool,
) -> None:
    """Test to verify structured output with strict=True."""

    from pydantic import BaseModel as BaseModelProper
    from pydantic import Field as FieldProper

    llm = ChatOpenAI(model=model, use_responses_api=use_responses_api)

    class Joke(BaseModelProper):
        """Joke to tell user."""

        setup: str = FieldProper(description="question to set up a joke")
        punchline: str = FieldProper(description="answer to resolve the joke")

    # Pydantic class
    chat = llm.with_structured_output(Joke, method=method, strict=True)
    result = chat.invoke("Tell me a joke about cats.")
    assert isinstance(result, Joke)

    for chunk in chat.stream("Tell me a joke about cats."):
        assert isinstance(chunk, Joke)

    # Schema
    chat = llm.with_structured_output(
        Joke.model_json_schema(), method=method, strict=True
    )
    result = chat.invoke("Tell me a joke about cats.")
    assert isinstance(result, dict)
    assert set(result.keys()) == {"setup", "punchline"}

    for chunk in chat.stream("Tell me a joke about cats."):
        assert isinstance(chunk, dict)
    assert isinstance(chunk, dict)  # for mypy
    assert set(chunk.keys()) == {"setup", "punchline"}


@pytest.mark.parametrize("use_responses_api", [False, True])
@pytest.mark.parametrize(("model", "method"), [("gpt-4o-2024-08-06", "json_schema")])
def test_nested_structured_output_strict(
    model: str, method: Literal["json_schema"], use_responses_api: bool
) -> None:
    """Test to verify structured output with strict=True for nested object."""

    from typing import TypedDict

    llm = ChatOpenAI(model=model, temperature=0, use_responses_api=use_responses_api)

    class SelfEvaluation(TypedDict):
        score: int
        text: str

    class JokeWithEvaluation(TypedDict):
        """Joke to tell user."""

        setup: str
        punchline: str
        self_evaluation: SelfEvaluation

    # Schema
    chat = llm.with_structured_output(JokeWithEvaluation, method=method, strict=True)
    result = chat.invoke("Tell me a joke about cats.")
    assert isinstance(result, dict)
    assert set(result.keys()) == {"setup", "punchline", "self_evaluation"}
    assert set(result["self_evaluation"].keys()) == {"score", "text"}

    for chunk in chat.stream("Tell me a joke about cats."):
        assert isinstance(chunk, dict)
    assert isinstance(chunk, dict)  # for mypy
    assert set(chunk.keys()) == {"setup", "punchline", "self_evaluation"}
    assert set(chunk["self_evaluation"].keys()) == {"score", "text"}


@pytest.mark.parametrize(
    ("strict", "method"),
    [
        (True, "json_schema"),
        (False, "json_schema"),
        (True, "function_calling"),
        (False, "function_calling"),
    ],
)
def test_json_schema_openai_format(
    strict: bool, method: Literal["json_schema", "function_calling"]
) -> None:
    """Test we can pass in OpenAI schema format specifying strict."""
    llm = ChatOpenAI(model="gpt-5-nano")
    schema = {
        "name": "get_weather",
        "description": "Fetches the weather in the given location",
        "strict": strict,
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The location to get the weather for",
                },
                "unit": {
                    "type": "string",
                    "description": "The unit to return the temperature in",
                    "enum": ["F", "C"],
                },
            },
            "additionalProperties": False,
            "required": ["location", "unit"],
        },
    }
    chat = llm.with_structured_output(schema, method=method)
    result = chat.invoke("What is the weather in New York?")
    assert isinstance(result, dict)


def test_audio_output_modality() -> None:
    llm = ChatOpenAI(
        model="gpt-4o-audio-preview",
        temperature=0,
        model_kwargs={
            "modalities": ["text", "audio"],
            "audio": {"voice": "alloy", "format": "wav"},
        },
    )

    history: list[BaseMessage] = [
        HumanMessage("Make me a short audio clip of you yelling")
    ]

    output = llm.invoke(history)

    assert isinstance(output, AIMessage)
    assert "audio" in output.additional_kwargs

    history.append(output)
    history.append(HumanMessage("Make me a short audio clip of you whispering"))

    output = llm.invoke(history)

    assert isinstance(output, AIMessage)
    assert "audio" in output.additional_kwargs


def test_audio_input_modality() -> None:
    llm = ChatOpenAI(
        model="gpt-4o-audio-preview",
        temperature=0,
        model_kwargs={
            "modalities": ["text", "audio"],
            "audio": {"voice": "alloy", "format": "wav"},
        },
    )
    filepath = Path(__file__).parent / "audio_input.wav"

    audio_data = filepath.read_bytes()
    b64_audio_data = base64.b64encode(audio_data).decode("utf-8")

    history: list[BaseMessage] = [
        HumanMessage(
            [
                {"type": "text", "text": "What is happening in this audio clip"},
                {
                    "type": "input_audio",
                    "input_audio": {"data": b64_audio_data, "format": "wav"},
                },
            ]
        )
    ]

    output = llm.invoke(history)

    assert isinstance(output, AIMessage)
    assert "audio" in output.additional_kwargs

    history.append(output)
    history.append(HumanMessage("Why?"))

    output = llm.invoke(history)

    assert isinstance(output, AIMessage)
    assert "audio" in output.additional_kwargs


@pytest.mark.flaky(retries=3, delay=1)
def test_prediction_tokens() -> None:
    code = dedent(
        """
    /// <summary>
    /// Represents a user with a first name, last name, and username.
    /// </summary>
    public class User
    {
        /// <summary>
        /// Gets or sets the user's first name.
        /// </summary>
        public string FirstName { get; set; }

        /// <summary>
        /// Gets or sets the user's last name.
        /// </summary>
        public string LastName { get; set; }

        /// <summary>
        /// Gets or sets the user's username.
        /// </summary>
        public string Username { get; set; }
    }
    """
    )

    llm = ChatOpenAI(model="gpt-4.1-nano")
    query = (
        "Replace the Username property with an Email property. "
        "Respond only with code, and with no markdown formatting."
    )
    response = llm.invoke(
        [{"role": "user", "content": query}, {"role": "user", "content": code}],
        prediction={"type": "content", "content": code},
    )
    assert isinstance(response, AIMessage)
    assert response.response_metadata is not None
    output_token_details = response.response_metadata["token_usage"][
        "completion_tokens_details"
    ]
    assert output_token_details["accepted_prediction_tokens"] > 0
    assert output_token_details["rejected_prediction_tokens"] > 0


@pytest.mark.parametrize("use_responses_api", [False, True])
def test_stream_o_series(use_responses_api: bool) -> None:
    list(
        ChatOpenAI(model="o3-mini", use_responses_api=use_responses_api).stream(
            "how are you"
        )
    )


@pytest.mark.parametrize("use_responses_api", [False, True])
async def test_astream_o_series(use_responses_api: bool) -> None:
    async for _ in ChatOpenAI(
        model="o3-mini", use_responses_api=use_responses_api
    ).astream("how are you"):
        pass


class Foo(BaseModel):
    response: str


def test_stream_response_format() -> None:
    full: BaseMessageChunk | None = None
    chunks = []
    for chunk in ChatOpenAI(model="gpt-5-nano").stream(
        "how are ya", response_format=Foo
    ):
        chunks.append(chunk)
        full = chunk if full is None else full + chunk
    assert len(chunks) > 1
    assert isinstance(full, AIMessageChunk)
    parsed = full.additional_kwargs["parsed"]
    assert isinstance(parsed, Foo)
    assert isinstance(full.content, str)
    parsed_content = json.loads(full.content)
    assert parsed.response == parsed_content["response"]


async def test_astream_response_format() -> None:
    full: BaseMessageChunk | None = None
    chunks = []
    async for chunk in ChatOpenAI(model="gpt-5-nano").astream(
        "how are ya", response_format=Foo
    ):
        chunks.append(chunk)
        full = chunk if full is None else full + chunk
    assert len(chunks) > 1
    assert isinstance(full, AIMessageChunk)
    parsed = full.additional_kwargs["parsed"]
    assert isinstance(parsed, Foo)
    assert isinstance(full.content, str)
    parsed_content = json.loads(full.content)
    assert parsed.response == parsed_content["response"]


@pytest.mark.parametrize("use_responses_api", [False, True])
@pytest.mark.parametrize("use_max_completion_tokens", [True, False])
def test_o1(use_max_completion_tokens: bool, use_responses_api: bool) -> None:
    # o1 models need higher token limits for reasoning
    o1_token_limit = 1000
    if use_max_completion_tokens:
        kwargs: dict = {"max_completion_tokens": o1_token_limit}
    else:
        kwargs = {"max_tokens": o1_token_limit}
    response = ChatOpenAI(
        model="o1",
        reasoning_effort="low",
        use_responses_api=use_responses_api,
        **kwargs,
    ).invoke(
        [
            {"role": "developer", "content": "respond in all caps"},
            {"role": "user", "content": "HOW ARE YOU"},
        ]
    )
    assert isinstance(response, AIMessage)
    assert isinstance(response.text, str)
    assert response.text.upper() == response.text


@pytest.mark.scheduled
def test_o1_stream_default_works() -> None:
    result = list(ChatOpenAI(model="o1").stream("say 'hi'"))
    assert len(result) > 0


@pytest.mark.flaky(retries=3, delay=1)
def test_multi_party_conversation() -> None:
    llm = ChatOpenAI(model="gpt-5-nano")
    messages = [
        HumanMessage("Hi, I have black hair.", name="Alice"),
        HumanMessage("Hi, I have brown hair.", name="Bob"),
        HumanMessage("Who just spoke?", name="Charlie"),
    ]
    response = llm.invoke(messages)
    assert "Bob" in response.content


class ResponseFormat(BaseModel):
    response: str
    explanation: str


class ResponseFormatDict(TypedDict):
    response: str
    explanation: str


@pytest.mark.flaky(retries=3, delay=1)
@pytest.mark.parametrize(
    "schema", [ResponseFormat, ResponseFormat.model_json_schema(), ResponseFormatDict]
)
def test_structured_output_and_tools(schema: Any) -> None:
    llm = ChatOpenAI(model="gpt-5-nano", verbosity="low").bind_tools(
        [GenerateUsername], strict=True, response_format=schema
    )

    response = llm.invoke("What weighs more, a pound of feathers or a pound of gold?")
    if schema == ResponseFormat:
        parsed = response.additional_kwargs["parsed"]
        assert isinstance(parsed, ResponseFormat)
    else:
        parsed = json.loads(response.text)
        assert isinstance(parsed, dict)
        assert parsed["response"]
        assert parsed["explanation"]

    # Test streaming tool calls
    full: BaseMessageChunk | None = None
    for chunk in llm.stream(
        "Generate a user name for Alice, black hair. Use the tool."
    ):
        assert isinstance(chunk, AIMessageChunk)
        full = chunk if full is None else full + chunk
    assert isinstance(full, AIMessageChunk)
    assert len(full.tool_calls) == 1
    tool_call = full.tool_calls[0]
    assert tool_call["name"] == "GenerateUsername"


def test_tools_and_structured_output() -> None:
    llm = ChatOpenAI(model="gpt-5-nano").with_structured_output(
        ResponseFormat, strict=True, include_raw=True, tools=[GenerateUsername]
    )

    expected_keys = {"raw", "parsing_error", "parsed"}
    query = "Hello"
    tool_query = "Generate a user name for Alice, black hair. Use the tool."
    # Test invoke
    ## Engage structured output
    response = llm.invoke(query)
    assert isinstance(response["parsed"], ResponseFormat)
    ## Engage tool calling
    response_tools = llm.invoke(tool_query)
    ai_msg = response_tools["raw"]
    assert isinstance(ai_msg, AIMessage)
    assert ai_msg.tool_calls
    assert response_tools["parsed"] is None

    # Test stream
    aggregated: dict = {}
    for chunk in llm.stream(tool_query):
        assert isinstance(chunk, dict)
        assert all(key in expected_keys for key in chunk)
        aggregated = {**aggregated, **chunk}
    assert all(key in aggregated for key in expected_keys)
    assert isinstance(aggregated["raw"], AIMessage)
    assert aggregated["raw"].tool_calls
    assert aggregated["parsed"] is None


@pytest.mark.scheduled
def test_prompt_cache_key_invoke() -> None:
    """Test that `prompt_cache_key` works with invoke calls."""
    chat = ChatOpenAI(model="gpt-5-nano", max_completion_tokens=500)
    messages = [HumanMessage("Say hello")]

    # Test that invoke works with prompt_cache_key parameter
    response = chat.invoke(messages, prompt_cache_key="integration-test-v1")

    assert isinstance(response, AIMessage)
    assert isinstance(response.content, str)
    assert len(response.content) > 0

    # Test that subsequent call with same cache key also works
    response2 = chat.invoke(messages, prompt_cache_key="integration-test-v1")

    assert isinstance(response2, AIMessage)
    assert isinstance(response2.content, str)
    assert len(response2.content) > 0


@pytest.mark.scheduled
def test_prompt_cache_key_usage_methods_integration() -> None:
    """Integration test for `prompt_cache_key` usage methods."""
    messages = [HumanMessage("Say hi")]

    # Test keyword argument method
    chat = ChatOpenAI(model="gpt-5-nano", max_completion_tokens=10)
    response = chat.invoke(messages, prompt_cache_key="integration-test-v1")
    assert isinstance(response, AIMessage)
    assert isinstance(response.content, str)

    # Test model-level via model_kwargs
    chat_model_level = ChatOpenAI(
        model="gpt-5-nano",
        max_completion_tokens=10,
        model_kwargs={"prompt_cache_key": "integration-model-level-v1"},
    )
    response_model_level = chat_model_level.invoke(messages)
    assert isinstance(response_model_level, AIMessage)
    assert isinstance(response_model_level.content, str)


class BadModel(BaseModel):
    response: str

    @field_validator("response")
    @classmethod
    def validate_response(cls, v: str) -> str:
        if v != "bad":
            msg = 'response must be exactly "bad"'
            raise ValueError(msg)
        return v


# VCR can't handle parameterized tests
@pytest.mark.vcr
def test_schema_parsing_failures() -> None:
    llm = ChatOpenAI(model="gpt-5-nano", use_responses_api=False)
    try:
        llm.invoke("respond with good", response_format=BadModel)
    except Exception as e:
        assert e.response is not None  # type: ignore[attr-defined]
    else:
        raise AssertionError


# VCR can't handle parameterized tests
@pytest.mark.vcr
def test_schema_parsing_failures_responses_api() -> None:
    llm = ChatOpenAI(model="gpt-5-nano", use_responses_api=True)
    try:
        llm.invoke("respond with good", response_format=BadModel)
    except Exception as e:
        assert e.response is not None  # type: ignore[attr-defined]
    else:
        raise AssertionError


# VCR can't handle parameterized tests
@pytest.mark.vcr
async def test_schema_parsing_failures_async() -> None:
    llm = ChatOpenAI(model="gpt-5-nano", use_responses_api=False)
    try:
        await llm.ainvoke("respond with good", response_format=BadModel)
    except Exception as e:
        assert e.response is not None  # type: ignore[attr-defined]
    else:
        raise AssertionError


# VCR can't handle parameterized tests
@pytest.mark.vcr
async def test_schema_parsing_failures_responses_api_async() -> None:
    llm = ChatOpenAI(model="gpt-5-nano", use_responses_api=True)
    try:
        await llm.ainvoke("respond with good", response_format=BadModel)
    except Exception as e:
        assert e.response is not None  # type: ignore[attr-defined]
    else:
        raise AssertionError
